"""simple docstring"""



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
   import tensorflow as tf

if is_tensorflow_text_available():
   from transformers.models.bert import TFBertTokenizer


SCREAMING_SNAKE_CASE__:Optional[int]									=			["""bert-base-uncased""", """bert-base-cased"""]
SCREAMING_SNAKE_CASE__:List[str]									=			"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():






   class  snake_case__      (       tf.keras.Model       ):


    def __init__( self    ,		lowerCamelCase						):
           super().__init__()
           __a       =    tokenizer
           __a       =    AutoConfig.from_pretrained(lowerCamelCase						)
           __a       =    TFAutoModel.from_config(lowerCamelCase						)






    def 						a__   ( self    ,		lowerCamelCase						):
           __a       =    self.tokenizer(lowerCamelCase						)
           __a       =    self.bert(**lowerCamelCase						)
           return out["pooler_output"]






@require_tf
@require_tensorflow_text
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        super().setUp()

        __a       =    [
            BertTokenizer.from_pretrained(lowerCamelCase						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
        ]  # repeat for when fast_bert_tokenizer=false
        __a       =    [TFBertTokenizer.from_pretrained(lowerCamelCase						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
            TFBertTokenizer.from_pretrained(lowerCamelCase    ,		use_fast_bert_tokenizer=lowerCamelCase						)
            for checkpoint in TOKENIZER_CHECKPOINTS
        ]
        assert len(self.tokenizers						) == len(self.tf_tokenizers						)

        __a       =    [
            "This is a straightforward English test sentence.",
            "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
            "Now we're going to add some Chinese: 一 二 三 一二三",
            "And some much more rare Chinese: 齉 堃 齉堃",
            "Je vais aussi écrire en français pour tester les accents",
            "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
        ]
        __a       =    list(zip(self.test_sentences    ,		self.test_sentences[::-1]						)						)


 def 						a__   ( self						):
        for tokenizer, tf_tokenizer in zip(self.tokenizers    ,		self.tf_tokenizers						):
               for test_inputs in (self.test_sentences, self.paired_sentences):
                      __a       =    tokenizer(lowerCamelCase    ,		return_tensors="tf"    ,		padding="longest"						)
                      __a       =    tf_tokenizer(lowerCamelCase						)

                      for key in python_outputs.keys():
                             self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
                             self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]    ,		tf.intaa						) == tf_outputs[key]						)						)


 @slow
 def 						a__   ( self						):
        for tf_tokenizer in self.tf_tokenizers:
               __a       =    tf_tokenizer(self.paired_sentences						)
               __a       =    tf_tokenizer(
                   text=[sentence[0] for sentence in self.paired_sentences]    ,		text_pair=[sentence[1] for sentence in self.paired_sentences]    ,		)
               for key in merged_outputs.keys():
                      self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]    ,		tf.intaa						) == separated_outputs[key]						)						)


 @slow
 def 						a__   ( self						):
        for tf_tokenizer in self.tf_tokenizers:
               __a       =    tf.function(lowerCamelCase						)
               for test_inputs in (self.test_sentences, self.paired_sentences):
                      __a       =    tf.constant(lowerCamelCase						)
                      __a       =    compiled_tokenizer(lowerCamelCase						)
                      __a       =    tf_tokenizer(lowerCamelCase						)

                      for key in eager_outputs.keys():
                             self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)






 @slow
 def 						a__   ( self						):
        for tf_tokenizer in self.tf_tokenizers:
               __a       =    ModelToSave(tokenizer=lowerCamelCase						)
               __a       =    tf.convert_to_tensor(self.test_sentences						)
               __a       =    model(lowerCamelCase						)  # Build model with some sample inputs
               with TemporaryDirectory() as tempdir:
                      __a       =    Path(lowerCamelCase						) / "saved.model"
                      model.save(lowerCamelCase						)
                      __a       =    tf.keras.models.load_model(lowerCamelCase						)
               __a       =    loaded_model(lowerCamelCase						)
               # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
               self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)    ,		1E-5						)

"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
